Snapshot-QAOA: Extending QAOA to Quantum Hamiltonian Simulation

Snapshot-QAOA: Extending QAOA to Quantum Hamiltonian Simulation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Snapshot-QAOA: Extending QAOA to Quantum Hamiltonian Simulation Reuben Tate, Quinn Langfitt, Elijah Pelofske, Ammar Kirmani, Andreas Bärtschi, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9171925/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract We present Snapshot-QAOA, a variation of the Quantum Approximate Optimization Algorithm (QAOA) that finds approximate minimum energy eigenstates of a large set of quantum Hamiltonians. Traditionally, QAOA targets the task of approximately solving combinatorial optimization problems. Snapshot-QAOA enables a significant expansion of the use case space for QAOA to more general quantum Hamiltonians, where the goal is to approximate the ground-state. Snapshot-QAOA retains the desirable variational-algorithm qualities of QAOA such as a small parameter count and relatively shallow circuit depth. Snapshot-QAOA is thus a better trainable alternative to the (Near Intermediate-Scale Quantum) NISQ-era Variational Quantum Eigensolver (VQE) algorithm, while retaining a significant circuit-depth advantage over the (Quantum Error Corrected) QEC-era Quantum Phase Estimation (QPE) algorithm. Our fundamental approach is inspired by the idea of Trotterization of a continuous-time linear adiabatic anneal schedule. Snapshot-QAOA restricts the QAOA evolution to not phasing out the mixing Hamiltonian completely at the end of the evolution, instead evolving only a partial typical linear QAOA schedule, thus creating a type of snapshot of the typical QAOA evolution. In this way, the cost of the quantum Hamiltonian is encoded by the phase separator to address the diagonal terms in the problem Hamiltonian and by the mixer to address the non-diagonal terms in the problem Hamiltonian. We focus on QAOA with the transverse field mixer, and thus simulations of quantum Hamiltonians that contain single-site Pauli-X terms. By measuring the expectation value of the system in both X and Z-basis, we can estimate the ground-state energy of transverse field quantum Hamiltonians. The accuracy of ground-state energy finding with snapshot-QAOA is evaluated using extensive numerical simulations on a 16 qubit J1-J2 frustrated transverse field Ising model. QAOA Ground State Estimation Snapshot-QAOA Quantum Computing Quantum Algorithms Quantum Approximate Optimization Algorithm Quantum Simulation Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9171925","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":609050165,"identity":"1cb6f93d-c248-4d40-9dc0-4ca89a6ea958","order_by":0,"name":"Reuben 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